While many two-dimensional images can be viewed with the naked eye for simple analysis, many other two-dimensional images (e.g., acoustic, sonar, x-ray, infrared, etc.) must be carefully examined and analyzed. One of the most commonly examined/analyzed two-dimensional images is an x-ray of living beings or inanimate structures. For example, a mammogram is a common film x-ray usually taken with an x-ray machine dedicated to breast imaging. A mammogram usually has low contrast because of the similarity in optical density of breast tissue structures and because only a limited amount of ionizing radiation can be safely received by the patient. The mammogram image also has fairly low resolution due to inherent limits of the x-ray filming process, cost constraints, and the interrelationships of tissue structures in the three-dimensional breast. All of these issues make it difficult to detect breast malignancies, especially in the earliest stages thereof.
Currently, doctors are limited to examining a mammogram by visually examining the original x-ray backed by a light source. The only enhancements available are crude ones such as using a magnifying glass, tweaking the contrast on an image view, or filtering the image by blocking out a range of pixel intensity levels. Statistics indicate that an estimated twenty percent of malignancies present in a mammogram are missed by doctors, usually because they are too small or faint (i.e., low intensity) to be noticed on the initial screening or they were partially obscured by other imaged tissues. Also, the known difficulty of discerning small malignancies forces doctors to take a very conservative approach when reviewing a mammogram. Thus, biopsies are often ordered simply because the mammogram is not clear. However, in about eighty percent of patients sent for biopsy, no malignancy is found. As a result, thousands of unnecessary biopsies are performed each year. Each biopsy represents a risk to the patient and can cause the formation of scar tissue in the area of the biopsy that may obscure detection of future problems.
To aid in the analysis of two-dimensional images, a variety of computerized detection algorithms are being developed. To utilize the algorithm, the image is first digitized for processing purposes. In general, detection algorithms look at small pieces of the digital image to evaluate the possibility of the presence of an abnormality or, more generally, a “target” of interest. However, by looking at the image as a plurality of isolated pieces, detection algorithms are unable to evaluate the pieces in the context of (i.e., relative to) the whole image as a human does when viewing an image. Very often, pieces of the image that might be classified as targets by the detection algorithm are not targets if considered in context with the image as a whole.